ODSC - Open Data Science

OCTOBER 21, 2024

Fortunately, there are many tools for ML evaluation and frameworks designed to support responsible AI development and evaluation. This topic is closely aligned with the Responsible AI track at ODSC West  — an event where experts gather to discuss innovations and challenges in AI.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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Mlearning.ai

DECEMBER 31, 2023

Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). What is Explainable AI?

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Unite.AI

DECEMBER 1, 2023

Alex Ratner is the CEO & Co-Founder of Snorkel AI , a company born out of the Stanford AI lab. Snorkel AI makes AI development fast and practical by transforming manual AI development processes into programmatic solutions. This stands in contrast to—but works hand-in-hand with—model-centric AI.

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Pickl AI

JANUARY 29, 2024

As we navigate this landscape, the interconnected world of Data Science, Machine Learning, and AI defines the era of 2024, emphasising the importance of these fields in shaping the future. ’ As we navigate the expansive tech landscape of 2024, understanding the nuances between Data Science vs Machine Learning vs ai.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

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Flipboard

JUNE 20, 2023

Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. These are the mathematical formulas written to simulate functions of the brain, which underlie the AI programming.

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ODSC - Open Data Science

SEPTEMBER 26, 2024

Using AI to Detect Anomalies in Robotics at the Edge Integrating AI-driven anomaly detection for edge robotics can transform countless industries by enhancing operational efficiency and improving safety. Where do explainable AI models come into play? Here’s everything that you can watch on-demand whenever you like!

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The MLOps Blog

FEBRUARY 27, 2025

TL;DR Bias is inherent to building a ML model. Related Best Tools For ML Model Serving Read more Privacy penetration tests Conduct regular privacy penetration tests to identify vulnerabilities in your system. How to integrate transparency, accountability, and explainability? Bias exists on a spectrum. Lets get into it!

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Bias Humans are innately biased, and the AI we develop can reflect our biases. These systems inadvertently learn biases that might be present in the training data and exhibited in the machine learning (ML) algorithms and deep learning models that underpin AI development.

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ODSC - Open Data Science

OCTOBER 3, 2024

AI Development Lifecycle: Learnings of What Changed with LLMs Noé Achache | Engineering Manager & Generative AI Lead | Sicara Using LLMs to build models and pipelines has made it incredibly easy to build proof of concepts, but much more challenging to evaluate the models.

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